MIMO Cooperation model.ppt

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MIMO Cooperation model.ppt

Modeling the MIMO Propagation Channel Claude Oestges Microwave Laboratory, Université catholique de Louvain, Belgium Outline Motivation and introduction MIMO channel modeling Physical channel models Analytical (non physical) channel models A few challenges Use of multiple polarizations Antenna correlations vs. cross-channel correlations Validity of Kronecker structure and diagonal channels Key hole effect Motivation Introduction to MIMO MIMO = Multiple spaced or cross-polarized antennas at transmit (Tx) and receive (Rx) sides MIMO diversity (Alamouti scheme, ST trellis codes) Improve quality (SNIR, BER) through redundancy Spatial multiplexing (e.g. BLAST) Increase capacity/throughput (data rate) by opening parallel independent channels Introduction to MIMO Introduction to MIMO MIMO channel models Physical channel models Ray-tracing Physical-statistical methods Geometry-based stochastic models (Double-)directional channel models (D)DCM Analytical (non physical) channel models Channel covariance matrix (full model) Simplified or specific models Ray-tracing techniques Model features Buildings are represented by blocks with given material characteristics Path-loss, shadowing and multipaths are implicitly modelled together Geometrical optics: each mechanism is ray-modelled using Fresnel theory and Uniform Theory of Diffraction (UTD) antenna gain complex dyadic spreading and polarisation coefficient factor Physical-statistical methods (I) Ray-tracing is highly site-specific More general model obtained by combining A physical model, i.e. electromagnetic relationships between environmental and propagation variables Statistical distributions of the environmental parameters Advantages Wide parameter range validity (frequency, etc.) Reduced computational cost thanks to pre-calculation High statistical accuracy Physical-statistical methods (II) The link between physical and environmental parameters is established by applying a ray-tracing to

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